MCP Gateway

Top 5 MCP Gateways in 2026

Top 5 MCP Gateways in 2026

The Model Context Protocol (MCP), introduced by Anthropic in late 2024, has become the de-facto standard for AI agent-to-tool connectivity. Now adopted by OpenAI, Microsoft, and Google, MCP defines how LLM applications discover and interact with external tools, data sources, and workflows through a standardized interface.

But the protocol alone does not solve production challenges. When agents connect to real tools, every tool connection becomes a potential attack surface, every action becomes a compliance event, and most organizations have zero visibility into any of it. An MCP gateway provides the missing infrastructure layer: centralized authentication, audit logging, rate control, and observability that transforms experimental AI tools into production-ready services.

By 2026, Gartner projects that the majority of enterprise applications will integrate AI agents. As this adoption scales, MCP gateways have moved from nice-to-have tooling to mission-critical infrastructure. This guide evaluates the top five MCP gateways for 2026 based on performance, security, observability, and production readiness.

What an MCP Gateway Does

An MCP gateway acts as a central control plane between your AI agents and external MCP servers. Instead of each agent client maintaining direct, individual connections to every tool server, the gateway consolidates these interactions into a single managed layer. Core responsibilities include:

  • Authentication and access control: Defining which agents and users can access which tools with centralized credential management
  • Security enforcement: Blocking prompt injection, detecting data exfiltration, and enforcing guardrails across all tool interactions
  • Audit trails: Logging every tool call with full context for compliance and forensics
  • Observability: Real-time dashboards, alerts, and distributed tracing across the entire MCP ecosystem
  • Routing and load balancing: Intelligent request distribution across multiple MCP servers with failover and health checks

Without a gateway, teams face an N-by-M integration problem where every agent must be individually configured for every tool. At scale, this becomes unmanageable. The gateways below are evaluated against these requirements.

1. Bifrost - Best for High-Performance MCP with Full LLM Gateway Infrastructure

Bifrost is a high-performance, open-source AI gateway written in Go that provides native Model Context Protocol support alongside a complete LLM gateway infrastructure. Unlike MCP-only gateways, Bifrost unifies both LLM routing and MCP tool access through a single OpenAI-compatible API across 12+ providers.

MCP capabilities:

  • Native MCP integration: Enable AI models to use external tools including filesystem access, web search, databases, and custom services through a standardized MCP interface
  • Tool discovery and routing: Agents discover available tools through Bifrost's gateway layer, with centralized configuration for which tools are accessible to which teams and applications
  • Unified agent-to-tool and agent-to-model plane: A single gateway manages both LLM calls and MCP tool interactions, eliminating the need for separate infrastructure components

LLM gateway infrastructure:

  • Automatic fallbacks: Seamless failover across providers and models with zero downtime when any provider is rate-limited or unavailable
  • Semantic caching: Intelligent response caching based on meaning rather than exact text, reducing repeat API costs significantly
  • Load balancing: Distributes requests intelligently across multiple API keys and providers to avoid rate limit walls

Enterprise governance and security:

  • Hierarchical budget management: Virtual keys enable team-level, customer-level, and project-level cost controls with hard limits preventing overruns
  • HashiCorp Vault integration: Secure API key management for enterprise environments
  • SSO support: Google and GitHub authentication for team access control
  • Comprehensive audit trails: Every request and tool call logged with metadata for compliance

Observability:

  • Native Prometheus metrics: Export gateway telemetry into existing monitoring stacks with structured logging and distributed tracing
  • Real-time cost and usage analytics: Track token consumption, latency distributions, error rates, and cache hit rates per provider, model, and team
  • Integrated quality monitoring: Connect gateway traffic to Maxim AI's observability suite for automated quality checks using custom evaluators and LLM-as-a-judge metrics

Performance:

  • Under sustained traffic at 5,000 requests per second, Bifrost adds roughly 11 microseconds of gateway overhead. In agent workflows where a single user action triggers multiple LLM calls and tool interactions, that performance advantage compounds rapidly compared to Python-based gateways that add hundreds of microseconds to milliseconds.

Deployment:

Organizations like Clinc, Thoughtful, and Atomicwork rely on Bifrost for production AI infrastructure where both LLM routing and tool access run through a single governed control plane.

Best for: Engineering teams that need MCP tool access unified with LLM routing, enterprise governance, ultra-low latency, and native observability in a single gateway.

2. IBM ContextForge

IBM ContextForge is a production-grade open-source AI gateway, registry, and proxy that federates tools, agents, models, and APIs into a single endpoint. It runs as a fully compliant MCP server and supports multi-cluster environments on Kubernetes.

Key capabilities:

  • Multi-protocol support: Tools gateway for MCP, REST-to-MCP translation, and gRPC-to-MCP conversion alongside agent gateway support for the A2A protocol
  • Model gateway: LLM proxy with OpenAI API spec supporting 8+ providers including watsonx, OpenAI, Anthropic, and Ollama
  • Plugin extensibility: 40+ plugins for additional transports, protocols, and integrations
  • OpenTelemetry observability: Tracing with Phoenix, Jaeger, Zipkin, and other OTLP backends
  • Admin UI: Real-time management, configuration, and log monitoring with air-gapped deployment support
  • Scalable deployment: Docker or PyPI installation with Redis-backed caching and multi-cluster federation

Limitations: ContextForge's breadth of features introduces configuration complexity. Teams need familiarity with Kubernetes for production-scale deployments. Built-in LLM routing is functional but less performant than dedicated Go-based gateways under high concurrency. Enterprise governance features like hierarchical budget management and vault-backed key storage are not as mature.

Best for: Platform engineering teams that need open-source multi-protocol federation across MCP, A2A, REST, and gRPC with full infrastructure ownership.

3. Kong AI Gateway

Kong AI Gateway extends Kong's established API management platform to support MCP traffic through its plugin architecture, applying the same governance model and security posture to AI workloads.

Key capabilities:

  • AI MCP Proxy plugin: Protocol bridge translating between MCP and HTTP, allowing MCP clients to call existing APIs through Kong without application changes
  • MCP OAuth 2.1 authentication: Centralized authentication across all MCP servers aligned with the official MCP specification
  • Unified API and AI governance: Manage traditional API traffic, LLM traffic, and MCP traffic under the same governance framework
  • Enterprise security: Authentication, authorization, mTLS, API key rotation, and RBAC through Kong Konnect
  • Plugin ecosystem: Extend gateway behavior with custom logic for logging, analytics, and tracing

Limitations: Kong is powerful but not lightweight. Setup and customization assume deep familiarity with the Kong ecosystem. MCP support is delivered as a plugin layer on top of a general-purpose API gateway rather than a native MCP implementation. Teams without existing Kong infrastructure face significant ramp-up time. Pricing ties to Kong Konnect or Enterprise plans.

Best for: Enterprises already standardized on Kong that want to extend their existing API governance to MCP traffic without adopting a separate tool.

4. Traefik Hub

Traefik Hub brings its proven cloud-native approach to MCP with a security-focused architecture that layers MCP capabilities as middleware on existing Traefik infrastructure.

Key capabilities:

  • Triple Gate Pattern: Security architecture protecting AI model, MCP protocol, and underlying API layers simultaneously
  • On-Behalf-Of (OBO) authentication: OAuth 2.0 token exchange enabling user-context-aware MCP operations
  • Task-Based Access Control (TBAC): Dynamic authorization that adapts to operational context rather than static role definitions
  • MCP as middleware: Layer MCP capabilities on existing Traefik infrastructure without deploying separate gateway components
  • OpenTelemetry integration: MCP-specific metrics and tracing for observability

Limitations: Traefik Hub's MCP support is tightly coupled to its existing proxy infrastructure. Teams not already running Traefik face the overhead of adopting a new ingress controller alongside MCP gateway functionality. LLM routing, semantic caching, and budget management are not native capabilities. The platform focuses on security and authentication rather than comprehensive AI lifecycle features.

Best for: Cloud-native platform teams already standardized on Traefik that want to add MCP governance without introducing new infrastructure components.

5. Azure API Management MCP Gateway

Microsoft's Azure API Management provides MCP gateway functionality with native integration into Azure services including Entra ID, Azure Monitor, App Insights, and Container Apps.

Key capabilities:

  • Native Entra ID support: Existing Azure AD configurations work immediately for MCP authentication without additional setup
  • Two deployment paths: Open-source Kubernetes for teams wanting control, or Azure API Management integration for fully managed deployment
  • Azure Monitor integration: MCP request telemetry feeds directly into Azure's observability stack
  • AgentCore Gateway: Converts REST APIs and Lambda functions into MCP-compatible tools exposed through gateway endpoints
  • OAuth 2.1 alignment: Authentication aligned with the official MCP authorization specification

Limitations: The gateway is scoped to the Azure ecosystem. Teams running multi-cloud or non-Microsoft infrastructure get limited value. MCP-specific features like semantic caching, multi-provider LLM routing, and advanced cost controls are not native. Configuration requires familiarity with Azure API Management patterns and pricing models.

Best for: Azure-exclusive platform teams and Microsoft-centric organizations that want MCP gateway functionality integrated with their existing Entra ID and Azure monitoring investments.

How to Choose the Right MCP Gateway

Selecting the right MCP gateway depends on your infrastructure, team composition, and production requirements:

  • Unified LLM and MCP gateway with enterprise governance: Bifrost provides the most complete solution by unifying LLM routing and MCP tool access in a single high-performance control plane with built-in observability, budget management, and security
  • Open-source multi-protocol federation: IBM ContextForge offers the broadest protocol support for teams that need MCP, A2A, REST, and gRPC under one self-hosted roof
  • Extending existing API governance: Kong and Traefik Hub serve teams already standardized on those platforms that want to layer MCP support without adopting new infrastructure
  • Microsoft ecosystem integration: Azure API Management fits organizations fully invested in Azure and Entra ID

For most enterprise teams, the critical decision is whether you need a standalone MCP gateway or a unified control plane that manages both LLM calls and tool interactions. Separate gateways for LLM routing and MCP create operational fragmentation. A unified approach gives you a single view of cost, latency, quality, and governance across every AI interaction your organization makes.

Conclusion

MCP gateways have become essential infrastructure for any organization deploying AI agents that interact with external tools in production. The protocol standardizes agent-to-tool connectivity, but production-grade security, governance, observability, and performance require a dedicated gateway layer.

For teams that need MCP tool access, LLM routing, enterprise governance, and observability unified in a single high-performance gateway, Bifrost delivers 11 microsecond overhead at 5,000 RPS with zero-config deployment and native integration into the full AI quality lifecycle.

Ready to see Bifrost in action? Book a demo to learn how Bifrost can give your team a single control plane for LLM routing and MCP tool access with enterprise-grade governance and observability.

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